High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

SED-MVS		78.97 1	84.56 1	72.45 1	81.70 2	86.20 2	77.82 6	59.97 7	88.89 1	65.96 2	86.00 6	84.02 1	70.03 1	76.19 4	76.17 5	79.22 27	94.46 1

DVP-MVS		77.54 2	84.41 2	69.54 7	79.93 3	86.08 3	77.20 11	60.31 5	88.62 2	62.54 3	86.67 4	83.77 2	58.04 48	75.84 7	75.69 8	79.21 28	94.17 2
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
ME-MVS		76.71 3	81.90 5	70.66 3	77.07 7	81.13 14	78.23 4	61.85 3	85.73 6	61.71 4	89.05 3	80.80 4	63.14 10	72.50 23	73.33 15	81.99 4	90.74 11

SF-MVS		76.41 4	80.45 7	71.69 2	82.90 1	86.54 1	82.08 1	64.58 1	81.67 12	59.82 7	86.26 5	77.90 9	61.11 17	71.81 28	70.75 35	79.63 16	88.22 25

MSP-MVS		76.38 5	82.99 3	68.68 8	71.93 19	78.65 27	77.61 8	55.44 20	88.04 3	60.25 6	92.24 1	77.08 12	69.84 2	75.48 8	75.69 8	76.99 79	93.75 3
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DVP-MVS++		75.99 6	81.32 6	69.77 6	71.86 21	85.13 4	77.62 7	59.87 9	82.69 11	61.55 5	83.05 10	79.63 7	69.78 3	76.01 5	75.89 6	77.92 60	86.86 45

DPE-MVS		75.74 7	82.82 4	67.49 12	77.07 7	82.01 8	77.05 12	57.70 13	86.55 5	55.44 19	90.50 2	82.52 3	60.33 21	72.99 15	72.98 17	77.33 70	92.19 6
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		74.63 8	78.53 12	70.07 4	76.10 10	82.56 7	79.30 2	59.89 8	80.49 14	57.75 13	66.98 28	76.16 15	65.95 5	79.35 1	78.47 1	81.45 7	85.71 60

APDe-MVS		74.59 9	80.23 8	68.01 11	76.51 9	80.20 17	77.39 9	58.18 11	85.31 7	56.84 15	84.89 7	76.08 16	60.66 19	71.85 27	71.76 23	78.47 48	91.49 9
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MCST-MVS		74.06 10	77.71 15	69.79 5	78.95 4	81.99 9	76.33 13	62.16 2	75.89 21	52.96 27	64.37 33	73.30 23	65.66 6	77.49 2	77.43 3	82.67 1	93.51 4

CNVR-MVS		73.87 11	78.60 11	68.35 10	73.32 14	81.97 10	76.19 14	59.29 10	80.12 15	56.70 16	67.09 27	76.48 13	64.26 8	75.88 6	75.75 7	80.32 10	92.93 5

SMA-MVS		73.31 12	79.53 9	66.05 14	71.25 22	80.13 18	74.99 15	56.09 16	84.14 8	54.48 21	73.74 17	80.23 5	61.43 14	74.96 9	74.09 12	78.08 57	89.42 15
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CSCG		72.98 13	76.86 17	68.46 9	78.23 6	81.74 11	77.26 10	60.00 6	75.61 24	59.06 8	62.72 35	77.42 11	56.63 62	74.24 11	77.18 4	79.56 18	89.13 19

HPM-MVS++		72.44 14	78.73 10	65.11 15	71.88 20	77.31 49	71.98 23	55.67 18	83.11 10	53.59 25	75.90 13	78.49 8	61.00 18	73.99 12	73.31 16	76.55 85	88.97 20

APD-MVS		71.86 15	77.91 14	64.80 17	70.39 26	75.69 60	74.02 17	56.14 15	83.59 9	52.92 28	84.67 8	73.46 22	59.30 28	69.47 44	69.66 48	76.02 92	88.84 21
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		71.50 16	77.27 16	64.77 18	69.64 28	79.26 19	73.53 18	54.73 26	79.32 17	54.23 22	74.81 14	74.61 20	59.40 26	73.00 14	72.17 20	77.10 78	87.72 30

NCCC		71.36 17	75.44 20	66.60 13	72.46 17	79.18 21	74.16 16	57.83 12	76.93 19	54.19 23	63.47 34	71.08 28	61.30 16	73.56 13	73.70 13	79.69 15	90.19 12

train_agg		70.74 18	76.53 18	63.98 21	70.33 27	75.16 66	72.33 22	55.78 17	75.74 22	50.41 36	80.08 12	73.15 24	57.75 52	71.96 26	70.94 32	77.25 74	88.69 23

MGCNet		70.65 19	76.30 19	64.05 20	67.54 37	80.89 15	68.89 36	49.94 50	77.93 18	55.92 18	68.22 25	73.10 25	62.14 11	71.10 32	71.81 22	79.87 11	91.03 10

TSAR-MVS + MP.		70.28 20	75.09 21	64.66 19	69.34 30	64.61 158	72.60 21	56.29 14	80.73 13	58.36 11	84.56 9	75.22 18	55.37 71	69.11 53	69.45 51	75.97 94	81.97 99
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepPCF-MVS	62.48 1	70.07 21	78.36 13	60.39 47	62.38 60	76.96 52	65.54 76	52.23 34	87.46 4	49.07 37	74.05 16	76.19 14	59.01 31	72.79 19	71.61 25	74.13 145	89.49 14

SteuartSystems-ACMMP		69.78 22	74.76 22	63.98 21	73.45 13	78.56 28	73.13 20	55.24 23	70.68 36	48.93 39	70.43 22	69.10 30	54.00 79	72.78 21	72.98 17	79.14 33	88.74 22
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 23	72.84 24	63.98 21	68.87 34	75.09 67	71.87 24	51.22 38	73.50 28	58.17 12	68.05 26	68.67 31	57.79 51	70.49 37	69.23 57	75.98 93	84.84 72

SD-MVS		68.30 24	72.58 26	63.31 26	69.24 31	67.85 129	70.81 29	53.65 31	79.64 16	58.52 10	74.31 15	75.37 17	53.52 85	65.63 92	63.56 129	74.13 145	81.73 104
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
DELS-MVS		67.36 25	70.34 39	63.89 24	69.12 32	81.55 12	70.82 28	55.02 24	53.38 78	48.83 40	56.45 50	59.35 59	60.05 24	74.93 10	74.78 10	79.51 19	91.95 7
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
MP-MVS		67.34 26	73.08 23	60.64 42	66.20 40	76.62 54	69.22 35	50.92 40	70.07 37	48.81 41	69.66 23	70.12 29	53.68 82	68.41 64	69.13 59	74.98 121	87.53 34
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS	60.65 2	67.33 27	71.52 32	62.44 29	59.79 96	74.84 69	68.89 36	55.56 19	73.91 27	53.50 26	55.00 56	65.63 35	60.08 23	71.99 25	71.33 29	76.85 80	87.94 28
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP-MVS		67.22 28	72.08 28	61.56 35	66.76 38	73.58 78	71.41 25	52.98 32	69.92 39	43.85 77	70.58 21	58.75 61	56.76 60	72.90 17	71.88 21	77.57 65	86.94 44

CANet		67.21 29	71.83 30	61.83 31	64.51 46	79.25 20	66.72 67	48.73 58	68.49 44	50.63 35	61.40 39	66.47 33	61.44 13	69.31 49	69.90 41	78.94 41	88.00 26

CDPH-MVS		67.03 30	71.64 31	61.65 34	69.10 33	76.84 53	71.35 27	55.42 21	67.02 47	42.83 84	65.27 32	64.60 39	53.16 88	69.70 43	71.40 27	78.02 59	86.67 51

MAR-MVS		66.85 31	69.81 40	63.39 25	73.56 12	80.51 16	69.87 31	51.51 37	67.78 46	46.44 58	51.09 74	61.60 53	60.38 20	72.67 22	73.61 14	78.59 44	81.44 108
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
DeepC-MVS_fast	60.18 3	66.84 32	70.69 37	62.36 30	62.76 55	73.21 81	67.96 42	52.31 33	72.26 31	51.03 30	56.50 49	64.26 40	63.37 9	71.64 29	70.85 33	76.70 83	86.10 57
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		66.77 33	72.21 27	60.44 46	61.23 82	70.00 109	64.26 81	47.79 80	72.98 29	56.32 17	71.35 20	72.33 26	55.68 70	65.49 93	66.66 87	77.35 68	86.62 52

ACMMPR		66.20 34	71.51 33	60.00 56	65.34 44	74.04 73	69.39 33	50.92 40	71.97 32	46.04 61	66.79 29	65.68 34	53.07 89	68.93 56	69.12 60	75.21 115	84.05 80

3Dnovator	58.39 4	65.97 35	66.85 55	64.94 16	73.72 11	79.03 22	67.73 47	54.25 27	61.52 54	52.79 29	42.27 121	60.73 57	62.01 12	71.29 30	71.75 24	79.12 34	81.34 111

TSAR-MVS + ACMM		65.95 36	72.83 25	57.93 72	69.35 29	65.85 149	73.36 19	39.84 186	76.00 20	48.69 42	82.54 11	75.03 19	49.38 117	65.33 95	63.42 131	66.94 207	81.67 105

sasdasda		65.55 37	70.75 35	59.49 64	62.11 67	78.26 37	66.52 69	43.82 144	71.54 33	47.84 46	61.30 40	61.68 50	58.48 39	67.56 74	69.67 46	78.16 55	85.25 67

canonicalmvs		65.55 37	70.75 35	59.49 64	62.11 67	78.26 37	66.52 69	43.82 144	71.54 33	47.84 46	61.30 40	61.68 50	58.48 39	67.56 74	69.67 46	78.16 55	85.25 67

QAPM		65.47 39	67.82 47	62.72 28	72.56 15	81.17 13	67.43 53	55.38 22	56.07 71	43.29 82	43.60 113	65.38 37	59.10 29	72.20 24	70.76 34	78.56 45	85.59 64

PGM-MVS		65.35 40	70.43 38	59.43 66	65.78 42	73.75 75	69.41 32	48.18 70	68.80 43	45.37 68	65.88 31	64.04 41	52.68 96	68.94 55	68.68 68	75.18 116	82.93 89

PHI-MVS		65.17 41	72.07 29	57.11 84	63.02 53	77.35 48	67.04 63	48.14 75	68.03 45	37.56 113	66.00 30	65.39 36	53.19 87	70.68 34	70.57 37	73.72 153	86.46 55

CLD-MVS		64.69 42	67.25 49	61.69 33	68.22 36	78.33 33	63.09 85	47.59 83	69.64 40	53.98 24	54.87 57	53.94 79	57.87 49	72.79 19	71.34 28	79.40 24	69.87 193
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		64.66 43	67.11 52	61.80 32	71.04 23	77.91 43	62.75 88	54.78 25	51.43 83	47.54 48	53.77 60	54.85 75	56.84 58	70.59 35	71.50 26	77.86 61	89.70 13

EPNet		64.39 44	70.93 34	56.77 86	60.58 91	75.77 57	59.28 111	50.58 44	69.93 38	40.73 102	68.59 24	61.60 53	53.72 80	68.65 59	68.07 72	75.75 106	83.87 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		64.37 45	69.48 41	58.39 69	62.21 64	71.81 101	67.27 58	49.51 52	69.40 42	45.76 66	60.41 43	64.96 38	51.84 98	67.33 81	67.57 80	73.78 152	84.89 70

EC-MVSNet		64.30 46	68.19 43	59.76 60	62.97 54	75.31 64	67.26 59	44.19 138	60.73 57	47.52 50	55.84 52	62.12 48	57.67 53	70.71 33	67.47 81	78.97 39	85.13 69

casdiffmvs_mvg		64.26 47	67.60 48	60.36 48	62.26 63	78.54 29	69.39 33	48.33 68	56.54 66	45.36 69	52.86 64	57.36 66	58.42 41	70.28 38	70.24 39	78.43 50	87.39 38
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		63.87 48	67.08 53	60.12 55	60.90 87	78.29 36	67.91 44	48.01 78	55.89 73	44.97 72	50.45 77	56.94 67	59.54 25	70.17 41	69.81 43	79.41 23	87.99 27
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E2		63.83 49	66.23 59	61.02 38	62.29 62	78.81 25	67.95 43	48.45 65	52.32 80	48.38 43	51.97 66	53.76 80	59.35 27	69.39 47	69.76 45	79.70 14	87.62 33

MVS_Test		63.75 50	67.24 50	59.68 61	60.01 92	76.99 51	68.13 40	45.17 122	57.45 65	43.74 79	53.07 63	56.16 72	61.33 15	70.27 39	71.11 30	79.72 13	85.63 63

X-MVS		63.53 51	68.62 42	57.60 76	64.77 45	73.06 83	65.82 74	50.53 45	65.77 49	42.02 95	58.20 47	63.42 44	47.83 128	68.25 69	68.50 69	74.61 132	83.16 86

viewcassd2359sk11		63.49 52	65.78 63	60.83 40	62.14 66	78.68 26	67.83 46	48.34 67	51.06 85	47.99 45	51.10 73	53.41 81	59.09 30	69.12 52	69.58 49	79.58 17	87.49 35

viewmanbaseed2359cas		63.30 53	65.85 62	60.31 49	61.55 77	78.41 32	68.44 38	47.39 89	50.91 86	46.42 59	50.98 76	53.99 78	58.60 37	69.11 53	70.10 40	79.48 20	87.46 36

ACMMP		63.27 54	67.85 46	57.93 72	62.64 58	72.30 96	68.23 39	48.77 57	66.50 48	43.05 83	62.07 36	57.84 64	49.98 109	66.58 87	66.46 94	74.93 122	83.17 84
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CS-MVS		63.16 55	68.01 45	57.49 78	57.39 112	72.73 90	63.38 84	45.16 123	59.37 59	46.49 57	58.93 46	57.68 65	56.31 64	71.12 31	70.37 38	76.23 91	85.88 58

E3new		63.04 56	65.16 68	60.56 44	61.92 70	78.50 30	67.67 49	48.17 71	49.34 93	47.42 52	49.85 80	52.98 83	58.75 33	68.79 57	69.33 52	79.43 21	87.30 39

E3		63.03 57	65.15 69	60.56 44	61.92 70	78.49 31	67.68 48	48.17 71	49.33 94	47.43 51	49.85 80	52.99 82	58.75 33	68.79 57	69.32 53	79.43 21	87.30 39

viewdifsd2359ckpt13		62.95 58	65.29 66	60.21 50	62.21 64	78.86 23	67.26 59	48.16 73	50.15 89	45.82 65	50.17 79	51.84 93	58.68 36	69.24 50	69.88 42	79.15 32	86.86 45

ETV-MVS		62.88 59	68.18 44	56.70 87	58.47 104	74.89 68	60.26 103	43.96 141	58.27 64	42.37 91	61.47 38	56.56 68	57.80 50	68.00 72	68.74 66	77.34 69	89.33 18

AdaColmap		62.79 60	62.63 87	62.98 27	70.82 24	72.90 87	67.84 45	54.09 29	65.14 50	50.71 33	41.78 123	47.64 127	60.17 22	67.41 80	66.83 85	74.28 138	76.69 138

3Dnovator+	55.76 7	62.70 61	65.10 70	59.90 57	65.89 41	72.15 97	62.94 87	49.82 51	62.77 53	49.06 38	43.62 112	61.47 55	58.60 37	68.51 60	66.75 86	73.08 167	80.40 119

OpenMVS	55.62 8	62.57 62	63.76 82	61.19 37	72.13 18	78.84 24	64.42 79	50.51 46	56.44 68	45.67 67	36.88 153	56.51 69	56.66 61	68.28 68	68.96 63	77.73 63	80.44 118

PVSNet_BlendedMVS		62.53 63	66.37 57	58.05 70	58.17 105	75.70 58	61.30 96	48.67 61	58.67 60	50.93 31	55.43 54	49.39 116	53.01 91	69.46 45	66.55 90	76.24 89	89.39 16

PVSNet_Blended		62.53 63	66.37 57	58.05 70	58.17 105	75.70 58	61.30 96	48.67 61	58.67 60	50.93 31	55.43 54	49.39 116	53.01 91	69.46 45	66.55 90	76.24 89	89.39 16

MVSTER		62.51 65	67.22 51	57.02 85	55.05 137	69.23 117	63.02 86	46.88 98	61.11 56	43.95 76	59.20 45	58.86 60	56.80 59	69.13 51	70.98 31	76.41 87	82.04 96

viewdifsd2359ckpt09		62.50 66	64.48 72	60.19 53	61.23 82	77.58 45	67.62 50	48.43 66	51.16 84	47.53 49	51.23 72	51.93 90	58.78 32	67.17 83	65.88 99	77.54 66	86.38 56

E5new		62.48 67	64.43 74	60.20 51	61.57 74	78.31 34	67.43 53	48.06 76	47.28 107	46.73 54	48.48 90	52.64 86	58.20 44	68.45 61	69.07 61	79.20 29	86.77 49

E5		62.48 67	64.43 74	60.20 51	61.57 74	78.31 34	67.43 53	48.06 76	47.28 107	46.73 54	48.48 90	52.64 86	58.20 44	68.45 61	69.07 61	79.20 29	86.77 49

CHOSEN 1792x2688		62.48 67	64.06 79	60.64 42	72.50 16	84.18 5	62.43 89	53.77 30	47.90 106	39.85 106	25.15 222	44.76 144	53.72 80	77.29 3	77.61 2	81.60 6	91.53 8

CostFormer		62.45 70	65.68 64	58.67 68	63.29 50	77.65 44	67.62 50	38.42 197	54.04 76	46.00 62	48.27 92	57.89 63	56.97 56	67.03 84	67.79 79	79.74 12	87.09 41

E4		62.36 71	64.27 77	60.14 54	61.58 73	78.25 39	67.38 56	47.91 79	46.78 112	46.58 56	48.07 93	52.52 88	58.23 43	68.32 66	68.96 63	79.19 31	86.98 43

PCF-MVS	55.99 6	62.31 72	66.60 56	57.32 81	59.12 103	73.68 77	67.53 52	48.71 59	61.35 55	42.83 84	51.33 71	63.48 43	53.48 86	65.64 91	64.87 113	72.22 172	85.83 59
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		62.30 73	66.05 60	57.92 74	57.08 114	75.60 63	66.90 64	47.06 96	55.45 75	43.37 81	53.45 62	55.60 73	57.21 55	66.57 88	68.00 75	75.89 97	87.70 32
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmacassd2359aftdt		62.09 74	64.24 78	59.58 63	60.94 85	78.01 40	68.04 41	46.83 100	46.59 114	45.11 71	47.34 95	52.79 84	57.50 54	68.43 63	69.54 50	79.08 35	87.01 42

E6new		61.98 75	63.77 80	59.90 57	61.52 79	78.01 40	67.09 61	47.57 86	45.71 118	45.97 63	46.87 97	51.47 96	58.17 46	68.20 70	69.31 55	79.07 36	86.81 47

E6		61.98 75	63.77 80	59.90 57	61.52 79	78.01 40	67.09 61	47.57 86	45.71 118	45.97 63	46.87 97	51.47 96	58.17 46	68.20 70	69.31 55	79.07 36	86.81 47

DI_MVS_pp		61.86 77	65.26 67	57.90 75	57.93 109	74.51 71	66.30 71	46.49 108	49.96 91	41.62 98	42.69 118	61.77 49	58.74 35	70.25 40	69.32 53	76.31 88	88.30 24

diffmvs_AUTHOR		61.85 78	65.54 65	57.54 77	56.64 119	75.64 62	66.65 68	46.55 107	53.31 79	42.72 88	51.70 68	55.51 74	56.91 57	66.66 85	68.09 71	75.77 105	87.89 29

MSLP-MVS++		61.81 79	62.19 92	61.37 36	68.33 35	63.08 179	70.75 30	38.89 193	63.96 52	57.51 14	48.59 87	61.66 52	53.67 83	62.04 143	59.92 181	79.03 38	76.08 141

SPE-MVS-test		61.68 80	65.97 61	56.67 88	57.77 110	72.59 93	57.63 119	45.54 117	58.53 63	47.11 53	59.45 44	56.34 70	55.15 72	64.52 107	65.03 111	76.80 81	85.34 66

OPM-MVS		61.59 81	62.30 91	60.76 41	66.53 39	73.35 80	71.41 25	54.18 28	40.82 149	41.57 99	45.70 104	54.84 76	54.43 76	69.92 42	69.19 58	76.45 86	82.25 93
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewdifsd2359ckpt07		61.43 82	63.33 85	59.20 67	61.66 72	77.47 47	66.75 66	46.85 99	45.54 120	45.32 70	48.59 87	51.61 95	56.09 66	67.46 76	68.01 74	78.54 47	84.67 74

MS-PatchMatch		61.41 83	61.88 96	60.85 39	70.57 25	75.98 56	66.29 72	46.91 97	50.56 88	48.28 44	36.30 156	51.64 94	50.95 104	72.89 18	70.65 36	82.13 3	75.17 152

casdiffseed414692147		60.92 84	62.03 93	59.63 62	62.33 61	76.41 55	67.31 57	47.59 83	48.83 101	43.83 78	41.47 124	47.12 132	58.26 42	67.43 79	68.40 70	78.47 48	84.57 77

viewmambaseed2359dif		60.68 85	63.59 84	57.29 82	56.93 115	75.24 65	65.36 78	45.82 115	49.89 92	43.57 80	49.83 82	51.89 92	56.33 63	64.86 102	65.71 101	75.75 106	87.72 30

EIA-MVS		60.56 86	64.29 76	56.20 93	59.14 102	72.68 92	59.55 109	43.56 148	51.78 82	41.01 101	55.47 53	51.93 90	55.87 67	65.01 99	66.57 89	78.06 58	86.60 54

ACMP	56.21 5	59.78 87	61.81 98	57.41 80	61.15 84	68.88 119	65.98 73	48.85 56	58.56 62	44.19 75	48.89 85	46.31 136	48.56 122	63.61 123	64.49 121	75.75 106	81.91 100
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		59.69 88	62.59 88	56.31 91	61.94 69	68.15 126	66.90 64	48.15 74	59.75 58	38.47 109	50.38 78	48.34 124	46.87 135	65.39 94	64.93 112	75.51 111	81.21 113

Effi-MVS+		59.63 89	61.78 99	57.12 83	61.56 76	71.63 102	63.61 82	47.59 83	47.18 109	37.79 110	45.29 105	49.93 112	56.27 65	67.45 77	67.06 83	75.91 95	83.93 81

CPTT-MVS		59.54 90	64.47 73	53.79 105	54.99 139	67.63 133	65.48 77	44.59 132	64.81 51	37.74 111	51.55 69	59.90 58	49.77 113	61.83 147	61.26 164	70.18 188	84.31 79

baseline2		59.20 91	61.72 100	56.27 92	59.61 98	74.12 72	58.65 114	49.42 53	48.10 104	40.12 105	49.10 84	44.15 146	51.24 101	66.65 86	67.88 78	78.56 45	82.06 95

MGCFI-Net		59.19 92	66.89 54	50.20 136	57.15 113	68.62 122	54.79 148	39.20 191	70.99 35	32.93 142	60.83 42	61.00 56	45.54 142	63.77 121	60.71 173	71.59 176	82.29 91

GeoE		58.97 93	60.94 101	56.67 88	61.27 81	72.71 91	61.35 95	45.69 116	49.19 98	41.22 100	39.55 140	49.58 115	52.79 95	64.79 103	65.89 98	77.73 63	84.87 71

baseline		58.65 94	61.99 94	54.75 100	54.70 141	71.85 100	60.20 104	43.91 142	55.99 72	40.13 104	53.50 61	50.91 109	55.76 68	61.29 155	61.73 156	73.83 149	78.68 130

PVSNet_Blended_VisFu		58.56 95	62.33 90	54.16 102	56.90 116	73.92 74	57.72 118	46.16 113	44.23 125	42.73 87	46.26 99	51.06 107	46.28 138	67.99 73	65.38 106	75.18 116	87.44 37

ACMM	53.73 9	57.91 96	58.27 121	57.49 78	63.10 51	66.45 143	65.65 75	49.02 55	53.69 77	42.67 89	36.41 155	46.07 139	50.38 107	64.74 105	64.63 118	74.14 144	75.91 142
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 97	63.63 83	51.15 123	52.18 148	70.20 108	58.14 117	37.32 204	56.49 67	31.06 152	57.38 48	50.05 111	53.67 83	64.98 101	65.04 110	74.57 133	81.29 112

ET-MVSNet_ETH3D		57.84 98	61.91 95	53.09 108	32.91 241	74.53 70	63.51 83	46.80 102	46.52 115	36.14 119	56.00 51	46.20 137	64.41 7	60.75 163	66.99 84	74.79 123	82.35 90

viewdifsd2359ckpt11		57.53 99	59.36 109	55.39 95	55.17 135	72.10 98	61.49 92	45.16 123	42.72 134	42.15 93	46.03 101	47.43 128	54.14 78	61.84 145	62.46 147	74.23 139	82.96 87

viewmsd2359difaftdt		57.53 99	59.36 109	55.39 95	55.17 135	72.10 98	61.49 92	45.16 123	42.72 134	42.15 93	46.03 101	47.42 129	54.15 77	61.84 145	62.46 147	74.23 139	82.96 87

tpm cat1		57.41 101	58.26 122	56.42 90	60.80 89	72.56 94	64.35 80	38.43 196	49.18 99	46.36 60	36.69 154	43.50 150	54.47 74	61.39 153	62.64 142	74.11 147	81.81 101

IB-MVS	53.15 10	57.33 102	59.02 113	55.37 97	60.83 88	77.11 50	54.51 149	50.10 49	43.22 131	42.82 86	40.50 130	37.61 171	44.67 152	59.27 177	69.81 43	79.29 26	85.59 64
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
tpmrst		57.23 103	59.08 112	55.06 98	59.91 94	70.65 106	60.71 99	35.38 215	47.91 105	42.58 90	39.78 135	45.45 141	54.44 75	62.19 140	62.82 139	77.37 67	84.73 73

baseline1		57.21 104	60.53 103	53.33 107	62.50 59	69.86 111	57.33 123	50.59 43	43.39 130	30.00 158	48.60 86	51.09 106	42.36 165	69.38 48	68.03 73	77.20 75	73.39 165

FA-MVS(training)		57.15 105	60.42 104	53.34 106	58.15 107	72.77 88	59.79 107	38.68 194	49.01 100	36.56 118	40.79 128	45.44 142	53.04 90	65.23 98	67.93 77	73.82 150	81.80 103

HyFIR lowres test		57.12 106	59.11 111	54.80 99	61.55 77	77.55 46	59.02 112	45.00 127	41.84 146	33.93 136	22.44 229	49.16 119	51.02 103	68.39 65	68.71 67	78.26 54	85.70 62

MVS_111021_LR		57.06 107	60.60 102	52.93 109	56.25 122	65.14 156	55.16 146	41.21 176	52.32 80	44.89 73	53.92 59	49.27 118	52.16 97	61.46 151	60.54 174	67.92 200	81.53 107

DCV-MVSNet		56.80 108	58.96 114	54.28 101	59.96 93	66.74 141	60.37 102	44.87 129	41.01 148	36.81 116	47.57 94	47.87 126	48.23 125	64.41 109	65.17 108	75.45 112	79.95 123

Anonymous20231211		56.40 109	57.00 134	55.70 94	59.78 97	72.49 95	61.29 98	46.83 100	40.50 151	40.46 103	22.12 231	49.73 113	51.07 102	64.39 110	65.30 107	74.74 126	84.44 78

0.4-1-1-0.2		56.13 110	60.14 105	51.44 120	45.97 189	73.09 82	56.79 133	45.39 118	47.03 110	34.23 128	43.14 117	51.20 105	47.33 131	63.12 127	63.30 132	78.95 40	80.11 121

0.3-1-1-0.015		56.04 111	60.09 106	51.32 121	46.02 187	73.04 86	56.64 134	45.36 119	46.70 113	34.01 132	43.24 115	51.25 101	46.98 134	63.12 127	63.20 135	78.90 42	80.11 121

PMMVS		55.74 112	62.68 86	47.64 157	44.34 202	65.58 153	47.22 193	37.96 200	56.43 69	34.11 130	61.51 37	47.41 130	54.55 73	65.88 90	62.49 146	67.67 202	79.48 125

Fast-Effi-MVS+		55.73 113	58.26 122	52.76 110	54.33 142	68.19 125	57.05 124	34.66 217	46.92 111	38.96 108	40.53 129	41.55 159	55.69 69	65.31 96	65.99 95	75.90 96	79.34 126

FC-MVSNet-train		55.68 114	57.00 134	54.13 103	63.37 48	66.16 145	46.77 197	52.14 35	42.36 140	37.67 112	48.50 89	41.42 161	51.28 100	61.58 150	63.22 134	73.56 155	75.76 145

FMVSNet3		55.66 115	59.68 108	50.96 125	50.59 162	66.49 142	57.57 120	46.61 104	49.30 95	28.77 163	39.61 136	51.42 98	43.85 157	68.29 67	68.80 65	78.35 53	73.86 155

0.4-1-1-0.1		55.63 116	59.73 107	50.85 126	45.99 188	72.77 88	56.11 140	45.23 121	45.84 117	33.32 140	42.60 119	51.06 107	45.68 141	62.99 132	62.97 138	78.76 43	79.90 124

OMC-MVS		55.48 117	61.85 97	48.04 156	41.55 210	60.32 197	56.80 128	31.78 237	75.67 23	42.30 92	51.52 70	54.15 77	49.91 111	60.28 168	57.59 193	65.91 210	73.42 163

tpm		54.94 118	57.86 127	51.54 119	59.48 100	67.04 137	58.34 116	34.60 219	41.93 145	34.41 126	42.40 120	47.14 131	49.07 120	61.46 151	61.67 160	73.31 162	83.39 83

GBi-Net		54.66 119	58.42 119	50.26 134	49.36 171	65.81 150	56.80 128	46.61 104	49.30 95	28.77 163	39.61 136	51.42 98	42.71 161	64.25 113	65.54 102	77.32 71	73.03 168

test1		54.66 119	58.42 119	50.26 134	49.36 171	65.81 150	56.80 128	46.61 104	49.30 95	28.77 163	39.61 136	51.42 98	42.71 161	64.25 113	65.54 102	77.32 71	73.03 168

test-LLR		54.62 121	58.66 117	49.89 141	51.68 154	65.89 147	47.88 187	46.35 109	42.51 137	29.84 159	41.41 125	48.87 120	45.20 145	62.91 134	64.43 122	78.43 50	84.62 75

dmvs_re		54.51 122	57.04 133	51.56 118	56.51 120	62.63 183	55.56 142	50.45 47	45.31 121	24.75 180	43.94 111	39.99 166	42.74 160	66.53 89	65.44 105	79.33 25	75.46 147

TSAR-MVS + COLMAP		54.37 123	62.43 89	44.98 174	34.33 231	58.94 204	54.11 154	34.15 228	74.06 26	34.57 125	71.63 19	42.03 158	47.88 127	61.26 156	57.33 198	64.83 213	71.74 179

EPMVS		54.07 124	56.06 140	51.75 117	56.74 118	70.80 104	55.32 144	34.20 225	46.46 116	36.59 117	40.38 132	42.55 153	49.77 113	61.25 157	60.90 169	77.86 61	70.08 190

v2v482		54.00 125	55.12 147	52.69 112	51.73 153	69.42 116	60.65 100	45.09 126	34.56 183	33.73 139	35.29 159	35.36 181	49.92 110	64.05 119	65.16 109	75.00 120	81.98 98

CNLPA		54.00 125	57.08 132	50.40 133	49.83 168	61.75 188	53.47 157	37.27 205	74.55 25	44.85 74	33.58 171	45.42 143	52.94 94	58.89 179	53.66 219	64.06 217	71.68 180

FMVSNet2		53.94 127	57.29 129	50.03 138	49.36 171	65.81 150	56.80 128	45.95 114	43.13 132	28.04 167	35.68 157	48.18 125	42.71 161	67.23 82	67.95 76	77.32 71	73.03 168

v8		53.77 128	54.82 152	52.54 113	52.12 149	66.95 140	60.56 101	43.23 154	37.17 172	35.35 121	34.96 162	37.50 173	49.51 116	63.67 122	64.59 119	74.48 135	78.91 129

GA-MVS		53.77 128	56.41 139	50.70 128	51.63 156	69.96 110	57.55 121	44.39 133	34.31 184	27.15 169	40.99 127	36.40 177	47.65 130	67.45 77	67.16 82	75.83 102	78.60 131

Effi-MVS+-dtu		53.63 130	54.85 151	52.20 115	59.32 101	61.33 191	56.42 137	40.24 184	43.84 127	34.22 129	39.49 141	46.18 138	53.00 93	58.72 183	57.49 197	69.99 191	76.91 136

thisisatest0530		53.61 131	57.22 130	49.40 146	51.30 158	68.22 124	52.72 165	43.34 152	42.72 134	35.31 122	43.57 114	44.14 147	44.37 155	63.00 131	64.86 114	69.34 194	74.00 154

v1144		53.47 132	54.65 153	52.10 116	51.93 151	69.81 112	59.32 110	44.77 131	33.21 190	32.52 144	33.55 172	34.34 190	49.29 118	64.58 106	64.81 116	74.74 126	82.27 92

blend_shiyan4		53.44 133	57.17 131	49.10 149	46.19 185	65.49 154	58.38 115	42.54 164	48.56 103	34.01 132	44.21 108	51.25 101	36.84 177	57.58 187	57.87 188	76.63 84	75.23 149

v10		53.44 133	54.40 154	52.31 114	52.08 150	66.99 138	59.68 108	43.41 149	35.90 178	34.30 127	33.98 169	35.56 179	50.10 108	64.39 110	64.67 117	74.32 136	79.30 127

PatchmatchNet		53.37 135	55.62 145	50.75 127	55.93 129	70.54 107	51.39 170	36.41 208	44.85 123	37.26 114	39.40 143	42.54 154	47.83 128	60.29 167	60.88 171	75.69 109	70.87 184
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test2506		53.36 136	57.36 128	48.68 152	55.53 131	68.11 127	54.31 151	46.25 111	43.54 128	22.21 192	40.19 133	43.69 149	36.56 181	64.15 117	65.94 96	77.20 75	75.91 142

IterMVS-LS		53.36 136	55.65 144	50.68 130	55.34 133	59.04 202	55.00 147	39.98 185	38.72 160	33.22 141	44.52 107	47.05 133	49.63 115	61.82 148	61.77 155	70.92 183	76.61 140
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 138	58.66 117	47.04 160	44.94 196	65.89 147	47.88 187	35.95 211	42.51 137	29.84 159	41.41 125	48.87 120	45.20 145	62.91 134	64.43 122	78.43 50	84.62 75

tttt0517		53.05 139	56.73 138	48.76 150	50.35 164	67.51 134	51.96 169	43.34 152	42.00 144	33.88 137	43.19 116	43.49 151	44.37 155	62.58 139	64.86 114	68.67 196	73.46 162

MDTV_nov1_ep13		52.99 140	55.59 146	49.95 140	54.08 143	70.69 105	56.47 136	38.42 197	42.78 133	30.19 157	39.56 139	43.31 152	45.78 140	60.07 172	62.11 152	74.74 126	70.62 185

EPP-MVSNet		52.91 141	58.91 115	45.91 166	54.99 139	68.84 120	49.27 177	42.71 162	37.53 166	20.20 204	46.09 100	56.19 71	36.90 176	61.37 154	60.90 169	71.41 177	81.41 109

dps		52.84 142	52.92 168	52.74 111	59.89 95	69.49 115	54.47 150	37.38 203	42.49 139	39.53 107	35.33 158	32.71 200	51.83 99	60.45 164	61.12 166	73.33 161	68.86 199

v1192		52.69 143	53.86 158	51.31 122	51.22 159	69.76 113	57.37 122	44.39 133	32.21 193	31.39 151	32.41 180	32.44 203	49.19 119	64.25 113	64.17 124	74.31 137	81.81 101

V42		52.63 144	55.08 148	49.76 143	44.93 197	67.49 136	60.19 105	42.13 172	37.21 171	34.08 131	34.57 165	37.30 174	47.29 132	63.48 125	64.15 125	69.96 192	81.38 110

MSDG		52.58 145	51.40 181	53.95 104	65.48 43	64.31 166	61.44 94	44.02 139	44.17 126	32.92 143	30.40 193	31.81 207	46.35 137	62.13 141	62.55 144	73.49 157	64.41 207

ECVR-MVS		52.52 146	55.88 142	48.60 153	55.53 131	68.11 127	54.31 151	46.25 111	43.54 128	21.75 196	32.76 177	39.83 169	36.56 181	64.15 117	65.94 96	77.20 75	76.81 137

Fast-Effi-MVS+-dtu		52.47 147	55.89 141	48.48 154	56.25 122	65.07 157	58.75 113	23.79 249	41.27 147	27.07 171	37.95 148	41.34 162	50.85 105	62.90 136	62.34 150	74.17 143	80.37 120

v144192		52.43 148	53.63 162	51.03 124	51.06 160	69.60 114	56.94 126	44.84 130	32.15 194	30.88 153	32.45 179	32.71 200	48.36 123	62.98 133	63.52 130	74.10 148	82.02 97

thres100view900		52.33 149	53.91 157	50.48 132	56.10 124	67.79 130	56.18 139	49.18 54	35.86 180	25.22 177	34.74 163	34.10 192	42.41 164	64.45 108	62.62 143	73.81 151	77.85 132

v1921920		51.95 150	53.19 164	50.51 131	50.82 161	69.14 118	55.45 143	44.34 137	31.53 198	30.53 155	31.96 182	31.67 208	48.31 124	63.12 127	63.28 133	73.59 154	81.60 106

v148		51.72 151	53.15 165	50.05 137	50.15 166	67.51 134	56.98 125	42.85 159	32.60 192	32.41 146	33.88 170	34.71 186	44.45 153	61.06 158	63.00 137	73.45 158	79.24 128

TAPA-MVS	47.92 11	51.66 152	57.88 126	44.40 178	36.46 225	58.42 207	53.82 156	30.83 239	69.51 41	34.97 124	46.90 96	49.67 114	46.99 133	58.00 186	54.64 214	63.33 223	68.00 201
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		51.53 153	57.98 125	44.01 182	55.96 128	66.16 145	47.65 189	42.84 161	39.82 155	19.09 212	44.97 106	50.28 110	27.20 220	63.43 126	63.84 126	71.33 179	77.33 134

v1240		51.42 154	52.66 170	49.97 139	50.31 165	68.70 121	54.05 155	43.85 143	30.78 202	30.22 156	31.43 186	31.03 215	47.98 126	62.62 138	63.16 136	73.40 159	80.93 115

pmmvs4		51.28 155	52.50 172	49.85 142	49.54 170	63.02 180	52.83 164	43.41 149	44.65 124	35.71 120	34.38 166	32.25 204	45.14 148	60.21 171	60.03 178	72.44 171	72.98 171

Vis-MVSNet		51.13 156	58.04 124	43.06 188	47.68 178	67.71 131	49.10 178	39.09 192	37.75 164	22.57 189	51.03 75	48.78 122	32.42 205	62.12 142	61.80 154	67.49 204	77.12 135
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 157	58.79 116	42.00 198	40.59 212	65.32 155	46.65 199	39.26 189	39.90 154	27.30 168	54.12 58	52.03 89	30.93 209	59.85 174	59.62 183	67.23 206	80.70 116
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
tfpn200view9		50.91 158	52.45 173	49.11 148	56.10 124	64.53 161	53.06 161	47.31 92	35.86 180	25.22 177	34.74 163	34.10 192	41.08 167	60.84 160	61.37 162	71.90 175	75.70 146

SCA		50.88 159	53.70 160	47.59 158	55.99 126	55.81 217	43.14 211	33.45 231	45.16 122	37.14 115	41.83 122	43.82 148	44.43 154	60.37 165	60.02 179	71.38 178	68.90 198

gg-mvs-nofinetune		50.82 160	55.83 143	44.97 175	60.63 90	75.69 60	53.40 158	34.48 221	20.05 245	6.93 243	18.27 238	52.70 85	33.57 194	70.50 36	72.93 19	80.84 8	80.68 117

thres200		50.76 161	52.52 171	48.70 151	55.98 127	64.60 159	55.29 145	47.34 90	33.91 187	24.36 181	34.33 167	33.90 194	37.27 174	60.84 160	62.41 149	71.99 173	77.63 133

test1111		50.62 162	54.98 150	45.55 169	53.84 145	68.48 123	48.99 179	47.25 93	40.60 150	15.64 222	31.51 185	38.32 170	33.01 201	64.34 112	66.62 88	74.55 134	74.95 153

usedtu_blend_shiyan5		50.54 163	53.98 155	46.52 162	33.34 234	64.26 168	56.80 128	42.26 166	28.39 211	34.01 132	44.21 108	51.25 101	36.84 177	56.84 197	57.68 189	75.86 98	75.23 149

thres400		50.39 164	52.22 174	48.26 155	55.02 138	66.32 144	52.97 162	48.33 68	32.68 191	22.94 187	33.21 174	33.38 199	37.27 174	62.74 137	61.38 161	73.04 168	75.81 144

EG-PatchMatch MVS		50.23 165	50.89 184	49.47 144	59.54 99	70.88 103	52.46 166	44.01 140	26.22 229	31.91 147	24.97 223	31.45 211	33.48 196	64.79 103	66.51 93	75.40 113	71.39 182

IterMVS		50.23 165	53.27 163	46.68 161	47.59 180	60.58 195	53.10 160	36.62 207	36.07 176	25.89 174	39.42 142	40.05 165	43.65 158	60.22 170	61.35 163	73.23 163	75.23 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		50.14 167	52.78 169	47.06 159	45.56 193	63.56 176	54.22 153	43.74 146	34.10 186	25.37 176	29.79 200	42.06 157	38.70 170	64.25 113	65.54 102	74.75 124	70.18 189

ACMH	47.82 13	50.10 168	49.60 190	50.69 129	63.36 49	66.99 138	56.83 127	52.13 36	31.06 201	17.74 219	28.22 210	26.24 231	45.17 147	60.88 159	63.80 127	68.91 195	70.00 192
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 169	56.99 136	41.52 201	52.79 146	57.06 210	41.44 216	43.13 155	56.13 70	19.24 211	52.11 65	48.38 123	22.14 228	58.19 185	58.38 186	70.35 186	68.71 200
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FE-MVSNET3		49.74 170	53.74 159	45.07 173	33.34 234	64.26 168	48.12 182	42.26 166	28.39 211	34.01 132	44.21 108	51.25 101	36.84 177	56.84 197	57.68 189	75.86 98	73.60 159

LS3D		49.59 171	49.75 189	49.40 146	55.88 130	59.86 199	56.31 138	45.33 120	48.57 102	28.32 166	31.54 184	36.81 176	46.27 139	57.17 192	55.88 209	64.29 216	58.42 226

usedtu_dtu_shiyan1		49.57 172	53.64 161	44.82 176	42.15 209	67.70 132	49.68 175	46.75 103	40.11 153	18.63 216	29.92 197	34.46 189	35.01 186	65.00 100	66.55 90	76.72 82	71.76 178

UniMVSNet_NR-MVSNet		49.56 173	53.04 166	45.49 170	51.59 157	64.42 165	46.97 194	51.01 39	37.87 162	16.42 220	39.87 134	34.91 185	33.43 198	59.59 175	62.70 140	73.52 156	71.94 174

CDS-MVSNet		49.25 174	53.97 156	43.75 184	47.53 181	64.53 161	48.59 180	42.27 165	33.77 188	26.64 172	40.46 131	42.26 156	30.01 212	61.77 149	61.71 157	67.48 205	73.28 167
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 175	51.75 177	46.10 165	42.78 207	55.60 220	53.11 159	34.46 222	55.69 74	32.47 145	34.16 168	41.45 160	48.91 121	57.13 193	54.09 216	64.84 212	64.10 208
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 176	48.86 200	49.44 145	56.75 117	62.01 187	56.62 135	47.55 88	37.49 167	23.98 182	26.68 216	29.46 222	43.12 159	57.45 191	58.85 185	68.62 197	70.05 191

NR-MVSNet		48.84 177	51.76 176	45.44 171	57.66 111	60.64 193	47.39 190	47.63 81	37.26 168	13.31 225	37.31 150	29.64 221	33.53 195	63.52 124	62.09 153	73.10 166	71.89 177

CR-MVSNet		48.82 178	51.85 175	45.29 172	46.74 183	55.95 215	52.06 167	34.21 223	42.17 141	31.74 148	32.92 176	42.53 155	45.00 149	58.80 180	61.11 167	61.99 229	69.47 194

thres600view7		48.44 179	50.23 187	46.35 164	54.05 144	64.60 159	50.18 173	47.34 90	31.73 197	20.74 202	32.28 181	32.62 202	33.79 193	60.84 160	56.11 207	71.99 173	73.40 164

test-mter		48.31 180	55.04 149	40.45 206	34.12 232	59.02 203	41.77 215	28.05 243	38.43 161	22.67 188	39.35 144	44.40 145	41.88 166	60.30 166	61.68 159	74.20 141	82.12 94

PatchT		48.11 181	51.27 183	44.43 177	50.13 167	61.58 189	33.59 230	32.92 233	40.38 152	31.74 148	30.60 192	36.93 175	45.00 149	58.80 180	61.11 167	73.19 164	69.47 194

TranMVSNet+NR-MVSNet		48.06 182	51.36 182	44.21 180	50.38 163	62.09 186	47.28 191	50.88 42	36.11 175	13.25 226	37.51 149	31.60 210	30.70 210	59.34 176	62.53 145	72.81 169	70.31 187

TransMVSNet (Re)		47.46 183	48.94 197	45.74 168	57.96 108	64.29 167	48.26 181	48.47 64	26.33 228	19.33 209	29.45 203	31.28 214	25.31 224	63.05 130	62.70 140	75.10 119	65.47 205

DU-MVS		47.33 184	50.86 185	43.20 187	44.43 200	60.64 193	46.97 194	47.63 81	37.26 168	16.42 220	37.31 150	31.39 212	33.43 198	57.53 189	59.98 180	70.35 186	71.94 174

v7n		47.22 185	48.38 202	45.87 167	48.20 177	63.58 175	50.69 171	40.93 180	26.60 227	26.44 173	26.52 217	29.65 220	38.19 172	58.22 184	60.23 177	70.79 184	73.83 156

UA-Net		47.19 186	53.02 167	40.38 207	55.31 134	60.02 198	38.41 222	38.68 194	36.42 174	22.47 191	51.95 67	58.72 62	25.62 223	54.11 213	53.40 220	61.79 230	56.51 230

Baseline_NR-MVSNet		47.14 187	50.83 186	42.84 190	44.43 200	63.31 178	44.50 207	50.36 48	37.71 165	11.25 231	30.84 189	32.09 205	30.96 208	57.53 189	63.73 128	75.53 110	70.60 186

pmmvs5		47.02 188	50.02 188	43.51 186	43.48 205	62.65 182	47.24 192	37.78 202	30.59 203	24.80 179	35.26 160	30.43 216	34.36 189	59.05 178	60.28 176	73.40 159	71.92 176

UniMVSNet (Re)		46.89 189	51.65 179	41.34 203	45.60 192	62.71 181	44.05 208	47.10 95	37.24 170	13.55 224	36.90 152	34.54 188	26.76 221	57.56 188	59.90 182	70.98 182	72.69 172

thisisatest0515		46.88 190	49.57 191	43.74 185	45.33 195	60.46 196	46.19 201	41.06 179	30.34 204	29.73 161	32.50 178	31.63 209	35.43 184	58.75 182	61.71 157	64.70 215	71.59 181

tfpnnormal		46.61 191	46.82 209	46.37 163	52.70 147	62.31 184	50.39 172	47.17 94	25.74 231	21.80 193	23.13 227	24.15 239	33.45 197	60.28 168	60.77 172	72.70 170	71.39 182

pm-mvs1		46.14 192	49.34 194	42.41 195	48.93 174	62.22 185	44.98 205	42.68 163	27.66 221	20.76 201	29.88 199	34.96 184	26.41 222	60.03 173	60.42 175	70.70 185	70.20 188

wanda-best-256-512		46.10 193	49.06 195	42.66 191	33.34 234	64.26 168	48.12 182	42.26 166	28.39 211	21.80 193	28.97 205	33.62 195	34.55 187	56.84 197	57.68 189	75.86 98	73.66 158

FE-blended-shiyan7		46.10 193	49.06 195	42.66 191	33.34 234	64.26 168	48.12 182	42.26 166	28.39 211	21.80 193	28.96 206	33.62 195	34.55 187	56.84 197	57.68 189	75.86 98	73.67 157

blended_shiyan6		45.97 195	48.94 197	42.50 194	33.34 234	64.17 172	47.94 186	42.22 170	28.07 218	21.66 198	28.80 207	33.57 197	34.06 190	56.79 202	57.55 195	75.79 103	73.60 159

blended_shiyan8		45.96 196	48.92 199	42.51 193	33.32 239	64.17 172	47.95 185	42.22 170	28.06 219	21.70 197	28.77 208	33.56 198	34.06 190	56.76 203	57.55 195	75.79 103	73.59 161

IterMVS-SCA-FT		45.87 197	51.55 180	39.24 210	46.22 184	59.43 200	52.89 163	31.93 234	36.01 177	23.68 183	38.86 145	39.88 168	39.05 169	56.25 206	58.17 187	41.70 250	72.25 173

MIMVSNet		45.62 198	49.56 192	41.02 204	38.17 216	64.43 164	49.48 176	35.43 214	36.53 173	20.06 206	22.58 228	35.16 183	28.75 217	61.97 144	62.20 151	74.20 141	64.07 209

gm-plane-assit		45.41 199	48.03 204	42.34 196	56.49 121	40.48 247	24.54 252	34.15 228	14.44 253	6.59 244	17.82 240	35.32 182	49.82 112	72.93 16	74.11 11	82.47 2	81.12 114

ADS-MVSNet		45.39 200	46.42 210	44.19 181	48.74 176	57.52 208	43.91 209	31.93 234	35.89 179	27.11 170	30.12 194	32.06 206	45.30 143	53.13 219	55.19 211	68.15 199	61.07 218

gbinet_0.2-2-1-0.02		45.07 201	48.55 201	41.00 205	30.59 244	63.61 174	46.97 194	41.88 173	25.18 233	18.93 215	27.74 213	34.25 191	32.89 202	56.40 205	57.32 199	74.75 124	75.37 148

GG-mvs-BLEND		44.87 202	64.59 71	21.86 246	0.01 264	73.70 76	55.99 141	0.01 261	50.70 87	0.01 266	49.18 83	63.61 42	0.01 260	63.83 120	64.50 120	75.13 118	86.62 52

pmmvs-eth3d		44.67 203	45.27 215	43.98 183	42.56 208	55.72 219	44.97 206	40.81 182	31.96 196	29.13 162	26.09 219	25.27 236	36.69 180	55.13 210	56.62 204	69.68 193	66.12 204

MDTV_nov1_ep13_2view		44.44 204	45.75 213	42.91 189	46.13 186	63.43 177	46.53 200	34.20 225	29.08 210	19.95 207	26.23 218	27.89 226	35.88 183	53.36 218	56.43 205	74.74 126	63.86 210

CMPMVS	33.64 16	44.39 205	46.41 211	42.03 197	44.21 203	56.50 213	46.73 198	26.48 248	34.20 185	35.14 123	24.22 224	34.64 187	40.52 168	56.50 204	56.07 208	59.12 234	62.74 214
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 206	51.67 178	35.72 220	51.82 152	55.24 221	34.57 229	41.63 174	39.10 158	8.84 238	45.93 103	46.63 135	14.45 240	54.09 214	57.03 201	63.00 224	63.65 211

TAMVS		44.27 207	49.35 193	38.35 214	44.74 198	61.04 192	39.07 220	31.82 236	29.95 206	18.34 217	33.55 172	39.94 167	30.01 212	56.85 196	57.58 194	66.13 209	66.54 202

MVS-HIRNet		43.98 208	43.63 219	44.39 179	47.66 179	59.31 201	32.66 236	33.88 230	30.15 205	33.75 138	16.82 245	28.39 225	45.25 144	53.92 217	55.00 213	73.16 165	61.80 215

UniMVSNet_ETH3D		43.97 209	46.01 212	41.59 199	38.31 215	56.20 214	49.69 174	38.18 199	28.18 215	19.88 208	27.82 212	30.20 217	33.41 200	54.18 212	56.30 206	70.05 190	69.17 196

RPMNet		43.70 210	48.17 203	38.48 213	45.52 194	55.95 215	37.66 224	26.63 247	42.17 141	25.47 175	29.59 202	37.61 171	33.87 192	50.85 224	52.02 224	61.75 231	69.00 197

PatchMatch-RL		43.37 211	44.93 216	41.56 200	37.94 217	51.70 223	40.02 218	35.75 212	39.04 159	30.71 154	35.14 161	27.43 228	46.58 136	51.99 220	50.55 228	58.38 236	58.64 224

FMVSNet5		43.29 212	47.07 207	38.87 211	30.46 245	50.99 225	45.87 202	37.19 206	42.17 141	19.32 210	26.77 215	40.51 163	30.26 211	56.82 201	55.81 210	70.10 189	56.46 231

test0.0.03 1		43.07 213	46.95 208	38.54 212	51.68 154	58.77 205	35.28 225	46.35 109	32.05 195	12.44 227	28.53 209	35.52 180	14.40 241	57.12 194	56.93 202	71.11 181	59.69 220

anonymousdsp		43.03 214	47.19 206	38.18 215	36.00 227	56.92 211	38.44 221	34.56 220	24.22 235	22.53 190	29.69 201	29.92 218	35.21 185	53.96 216	58.98 184	62.32 228	76.66 139

USDC		42.80 215	45.57 214	39.58 208	34.55 230	51.13 224	42.61 212	36.21 209	39.59 156	23.65 184	33.13 175	20.87 246	37.86 173	55.35 209	57.16 200	62.61 226	61.75 216

pmnet_mix02		42.41 216	43.24 222	41.44 202	45.80 191	57.46 209	42.19 213	41.57 175	29.38 208	23.39 185	26.08 220	23.96 240	27.31 219	51.50 221	53.76 218	68.36 198	60.58 219

CHOSEN 280x420		42.39 217	47.40 205	36.54 218	33.56 233	39.66 250	40.67 217	26.88 246	34.66 182	18.03 218	30.09 195	45.59 140	44.82 151	54.46 211	54.00 217	55.28 243	73.32 166

pmmvs6		41.90 218	44.01 218	39.43 209	44.45 199	58.77 205	41.92 214	39.22 190	21.74 238	19.08 213	17.40 243	31.33 213	24.28 226	55.94 207	56.67 203	67.60 203	66.24 203

Anonymous20231206		40.63 219	43.29 221	37.53 216	48.88 175	55.81 217	34.99 226	44.98 128	28.16 216	10.16 235	17.26 244	27.50 227	18.28 232	54.00 215	55.07 212	67.85 201	65.23 206

FE-MVSNET2		39.87 220	43.46 220	35.69 221	30.82 243	56.74 212	37.91 223	42.85 159	24.70 234	8.15 240	18.01 239	23.67 241	23.12 227	56.86 195	61.26 164	71.25 180	62.95 212

CVMVSNet		38.91 221	44.49 217	32.40 230	34.57 229	47.20 236	34.81 227	34.20 225	31.45 199	8.95 237	38.86 145	36.38 178	24.30 225	47.77 229	46.94 240	57.59 238	62.85 213

COLMAP_ROB	34.79 15	38.65 222	40.72 225	36.23 219	36.41 226	49.22 232	45.51 204	27.60 245	37.81 163	20.54 203	23.37 226	24.25 238	28.11 218	51.02 223	48.55 231	59.22 233	50.82 242
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 223	41.72 224	34.15 223	40.56 213	50.07 228	33.17 233	44.35 136	27.64 223	5.54 250	30.84 189	26.67 229	14.99 238	45.64 232	52.38 223	66.29 208	58.83 223

WR-MVS		37.61 224	42.15 223	32.31 232	43.64 204	51.85 222	29.39 243	43.35 151	27.65 222	4.40 252	29.90 198	29.80 219	10.46 245	46.73 231	51.98 225	62.60 227	57.16 228

TinyColmap		37.18 225	37.37 238	36.95 217	31.17 242	45.21 241	39.71 219	34.65 218	29.83 207	20.20 204	18.54 237	13.72 256	38.27 171	50.33 225	51.57 226	57.71 237	52.42 239

CP-MVSNet		37.09 226	40.62 226	32.99 225	37.56 219	48.25 233	32.75 234	43.05 156	27.88 220	5.93 246	31.27 187	25.82 234	15.09 236	43.37 239	48.82 229	63.54 221	58.90 221

DTE-MVSNet		36.91 227	40.44 227	32.79 228	40.74 211	47.55 235	30.71 241	44.39 133	27.03 225	4.32 253	30.88 188	25.99 232	12.73 243	45.58 233	50.80 227	63.86 218	55.23 234

PS-CasMVS		36.84 228	40.23 230	32.89 226	37.44 220	48.09 234	32.68 235	42.97 158	27.36 224	5.89 247	30.08 196	25.48 235	14.96 239	43.28 240	48.71 230	63.39 222	58.63 225

WR-MVS_H		36.29 229	40.35 229	31.55 234	37.80 218	49.94 230	30.57 242	41.11 178	26.90 226	4.14 254	30.72 191	28.85 223	10.45 246	42.47 242	47.99 235	65.24 211	55.54 232

SixPastTwentyTwo		36.11 230	37.80 234	34.13 224	37.13 223	46.72 239	34.58 228	34.96 216	21.20 241	11.66 228	29.15 204	19.88 247	29.77 214	44.93 234	48.34 232	56.67 240	54.41 236

test20.03		36.00 231	38.92 231	32.60 229	45.92 190	50.99 225	28.05 248	43.69 147	21.62 239	6.03 245	17.61 242	25.91 233	8.34 252	51.26 222	52.60 222	63.58 219	52.46 238

TDRefinement		35.76 232	38.23 232	32.88 227	19.09 255	46.04 240	43.29 210	29.49 240	33.49 189	19.04 214	22.29 230	17.82 251	29.69 216	48.60 227	47.24 238	56.65 241	52.12 240

LTVRE_ROB	32.83 17	35.10 233	37.46 235	32.35 231	43.12 206	49.99 229	28.52 245	33.23 232	12.73 255	8.18 239	27.71 214	21.34 244	32.64 204	46.92 230	48.11 233	48.41 247	55.45 233
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
PM-MVS		34.96 234	38.17 233	31.22 235	22.78 250	40.82 246	33.56 231	23.61 250	29.16 209	21.43 200	28.00 211	21.43 243	31.90 206	44.33 237	42.12 244	54.07 245	61.34 217

testgi		34.51 235	37.42 236	31.12 236	47.37 182	50.34 227	24.38 253	41.21 176	20.32 243	5.64 249	20.56 232	26.55 230	8.06 253	49.28 226	52.65 221	60.05 232	42.23 248

MDA-MVSNet-bldmvs		34.31 236	34.11 244	34.54 222	24.73 247	49.66 231	33.42 232	43.03 157	21.59 240	11.10 232	19.81 235	12.68 257	31.41 207	35.59 248	48.05 234	63.56 220	51.39 241

N_pmnet		34.09 237	35.74 241	32.17 233	37.25 222	43.17 244	32.26 238	35.57 213	26.22 229	10.60 234	20.44 234	19.38 250	20.20 230	44.59 236	47.00 239	57.13 239	49.35 245

RPSCF		33.61 238	40.43 228	25.65 242	16.00 257	32.41 252	31.73 240	13.33 257	50.13 90	23.12 186	31.56 183	40.09 164	32.73 203	41.14 246	37.05 247	36.99 253	50.63 243

FE-MVSNET		33.52 239	37.02 239	29.45 237	23.65 248	47.19 238	28.15 247	40.92 181	20.01 246	3.42 257	16.28 246	19.67 249	17.80 233	47.90 228	54.52 215	62.73 225	53.53 237

EU-MVSNet		33.00 240	36.49 240	28.92 238	33.10 240	42.86 245	29.32 244	35.99 210	22.94 236	5.83 248	25.29 221	24.43 237	15.21 235	41.22 245	41.65 246	54.08 244	57.01 229

pmmvs3		31.22 241	33.62 245	28.43 239	22.82 249	40.26 249	26.40 249	22.05 252	16.89 250	10.99 233	14.72 248	16.26 252	29.70 215	44.82 235	47.39 237	58.61 235	54.98 235

usedtu_dtu_shiyan2		31.12 242	34.28 243	27.44 241	11.70 258	47.20 236	32.04 239	31.41 238	14.11 254	8.15 240	13.22 250	19.80 248	16.49 234	42.54 241	45.42 242	64.82 214	57.66 227

FC-MVSNet-test		30.97 243	37.38 237	23.49 245	37.42 221	33.68 251	19.43 255	39.27 188	31.37 200	1.67 261	38.56 147	28.85 223	6.06 256	41.40 243	43.80 243	37.10 252	44.03 247

new-patchmatchnet		30.47 244	32.80 247	27.75 240	36.81 224	43.98 242	24.85 251	39.29 187	20.52 242	4.06 255	15.94 247	16.05 253	9.57 247	41.32 244	42.05 245	51.94 246	49.74 244

MIMVSNet1		29.60 245	33.37 246	25.20 244	19.52 253	43.94 243	26.29 250	37.92 201	19.95 247	3.79 256	12.64 253	21.99 242	7.70 254	43.83 238	46.32 241	55.97 242	44.92 246

FPMVS		26.87 246	28.19 248	25.32 243	27.09 246	29.49 254	32.28 237	17.79 254	28.09 217	11.33 229	19.38 236	14.69 254	20.88 229	35.11 249	32.82 250	42.56 249	37.75 249

WB-MVS		22.51 247	25.28 249	19.27 248	35.74 228	31.57 253	11.45 258	40.75 183	15.01 252	0.98 264	20.48 233	12.53 258	1.77 258	36.11 247	35.01 249	24.91 256	26.27 252

PMVS	18.18 18	21.95 248	22.85 250	20.90 247	21.92 251	14.78 256	19.95 254	17.31 255	15.69 251	11.32 230	13.70 249	13.91 255	15.02 237	34.92 250	31.72 251	39.85 251	35.20 250
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 249	22.71 251	14.89 250	10.93 260	24.08 255	14.22 256	13.94 256	18.68 248	2.93 258	12.84 252	11.27 259	11.94 244	30.57 252	30.58 252	35.38 254	30.93 251

Gipuma		17.16 250	17.83 252	16.36 249	18.76 256	12.15 259	11.97 257	27.78 244	17.94 249	4.86 251	2.53 260	2.73 264	8.90 250	34.32 251	36.09 248	25.92 255	19.06 255
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		13.92 251	17.14 253	10.16 253	1.69 263	6.92 262	11.25 259	5.74 258	22.41 237	8.11 242	10.40 254	20.91 245	13.73 242	22.17 253	13.98 255	20.44 257	23.18 253

PMMVS2		12.25 252	14.17 254	10.00 254	11.39 259	14.35 257	8.21 260	19.29 253	9.31 256	0.19 265	7.38 256	6.19 262	1.10 259	19.26 254	21.13 254	19.85 258	21.56 254

E-PMN		10.66 253	8.30 256	13.42 251	19.91 252	7.87 260	4.30 263	29.47 241	8.37 259	1.70 260	3.67 257	1.29 267	9.12 249	8.98 258	13.59 256	16.03 259	14.30 258

EMVS		10.15 254	7.67 257	13.05 252	19.22 254	7.77 261	4.48 261	29.34 242	8.65 258	1.67 261	3.55 258	1.36 266	9.15 248	8.15 259	11.79 258	14.44 260	12.43 259

MVE	10.35 19	9.76 255	11.08 255	8.22 255	4.43 261	13.04 258	3.36 264	23.57 251	5.74 260	1.76 259	3.09 259	1.75 265	6.78 255	12.78 256	23.04 253	9.44 261	18.09 256
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 256	0.01 258	0.00 257	0.00 265	0.00 265	0.00 267	0.00 262	0.01 261	0.00 267	0.02 261	0.00 268	0.00 262	0.01 260	0.01 259	0.00 264	0.03 260

test123		0.01 256	0.01 258	0.00 257	0.00 265	0.00 265	0.00 267	0.00 262	0.01 261	0.00 267	0.02 261	0.00 268	0.01 260	0.00 261	0.01 259	0.00 264	0.03 260

uanet_test		0.00 258	0.00 260	0.00 257	0.00 265	0.00 265	0.00 267	0.00 262	0.00 263	0.00 267	0.00 263	0.00 268	0.00 262	0.00 261	0.00 261	0.00 264	0.00 262

sosnet-low-res		0.00 258	0.00 260	0.00 257	0.00 265	0.00 265	0.00 267	0.00 262	0.00 263	0.00 267	0.00 263	0.00 268	0.00 262	0.00 261	0.00 261	0.00 264	0.00 262

sosnet		0.00 258	0.00 260	0.00 257	0.00 265	0.00 265	0.00 267	0.00 262	0.00 263	0.00 267	0.00 263	0.00 268	0.00 262	0.00 261	0.00 261	0.00 264	0.00 262

TestfortrainingZip							78.23 4	61.85 3		68.16 1						81.99 4

TPM-MVS					78.45 5	83.50 6	78.26 3			58.88 9	72.62 18	77.54 10	69.42 4			80.40 9	85.71 60
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										21.59 199

9.14												80.07 6

SR-MVS					63.74 47			48.51 63				73.80 21

Anonymous202405211			56.81 137		60.91 86	73.48 79	59.82 106	48.68 60	39.26 157		24.00 225	46.77 134	50.73 106	65.28 97	65.72 100	75.37 114	83.17 84

our_test_3					49.68 169	61.50 190	45.84 203

ambc			35.52 242		38.36 214	40.40 248	28.38 246		25.20 232	14.87 223	13.22 250	7.54 261	19.34 231	55.63 208	47.79 236	47.91 248	58.89 222

MTAPA										54.82 20		71.98 27

MTMP										50.64 34		68.31 32

Patchmatch-RL test							0.69 266

tmp_tt				4.41 256	2.56 262	1.81 264	2.61 265	0.27 260	20.12 244	9.81 236	17.69 241	9.04 260	1.96 257	12.88 255	12.11 257	9.23 262

XVS					62.70 56	73.06 83	61.80 90			42.02 95		63.42 44				74.68 130

X-MVStestdata					62.70 56	73.06 83	61.80 90			42.02 95		63.42 44				74.68 130

mPP-MVS					63.08 52							62.34 47

NP-MVS									72.62 30

Patchmtry						64.49 163	52.06 167	34.21 223		31.74 148

DeepMVS_CX						5.87 263	4.32 262	1.74 259	9.04 257	1.30 263	7.97 255	3.16 263	8.56 251	9.74 257		6.30 263	14.51 257

High-resolution multi-view benchmark